ReviewMilitary Medical Research2023
Deep learning-based lung sound analysis for intelligent stethoscope.
Review in Military Medical Research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 71 citations in OpenAlex.
- Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Audio-based digital biomarkers in diagnosing and managing respiratory diseases: a systematic review and bibliometric analysis.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- Enhancing pediatric cardiac auscultation with data-driven murmur detection: toward tele-consultation applications.Pediatric research · 2026Article
- An Interpretable Multidimensional Acoustic Physiology Map for COPD Using Digital Lung Sounds.Chronic obstructive pulmonary diseases (Miami, Fla.) · 2026Article
- Multimodal Deep Learning Approaches for Lung Disease Detection: A Review.Medicina (Kaunas, Lithuania) · 2026Review
- Respiratory sound analysis in a rabbit tracheomalacia model.Scientific reports · 2026Article
- Adaptive federated learning with differential privacy for multi-class respiratory disease recognition from lung sound recordings.Frontiers in digital health · 2026Article
- Artificial Intelligence Enabled Lung Sound Auscultation in the Early Diagnosis and Subtyping of Interstitial Lung Disease.Journal of clinical medicine · 2025Review
- Deep Learning-Driven Early Diagnosis of Respiratory Diseases using CNN-RNN Fusion on Lung Sound Data.Scientific reports · 2025Article
- Article
- Construction and Validation of an Automatic Segmentation Method for Respiratory Sound Time Labels.The clinical respiratory journal · 2025Article
- Streamlining heart failure patient care with machine learning of thoracic cavity sound data.World journal of cardiology · 2025Review
- Non-invasive acoustic classification of adult asthma using an XGBoost model with vocal biomarkers.Scientific reports · 2025Article
- Ensemble learning for biomedical signal classification: a high-accuracy framework using spectrograms from percussion and palpation.Scientific reports · 2025Article
- An explainable and accurate transformer-based deep learning model for wheeze classification utilizing real-world pediatric data.Scientific reports · 2025Article
- Automated system for diagnosing pulmonary fibrosis using crackle analysis in recorded lung sounds based on iterative envelope mean fractal dimension filter.Physiological measurement · 2025Article
- E-RespiNet: An LLM-ELECTRA driven triple-stream CNN with feature fusion for asthma classification.PloS one · 2025Article
- Feasibility and Acceptability of Pediatric Smartphone Lung Auscultation by Parents: Cross-Sectional Study.JMIR pediatrics and parenting · 2024Article
- CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD.Military Medical Research · 2024Article
- Dendrites contribute to the gradient of intrinsic timescales encompassing cortical and subcortical brain networks.Frontiers in cellular neuroscience · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Auscultation is crucial for the diagnosis of respiratory system diseases. However, traditional stethoscopes have inherent limitations, such as inter-listener variability and subjectivity, and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine. The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education. On this basis, machine learning, particularly deep learning, enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes. This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence (AI) in this field. We focus on each component of deep learning-based lung sound analysis systems, including the task categories, public datasets, denoising methods, and, most importantly, existing deep learning methods, i.e., the state-of-the-art approaches to convert lung sounds into two-dimensional (2D) spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds. Additionally, this review highlights current challenges in this field, including the variety of devices, noise sensitivity, and poor interpretability of deep models. To address the poor reproducibility and variety of deep learning in this field, this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension: https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis .
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.